Role of dynamics in neural computations underlying visual processing

Laurent Perrinet

Acknowledgements:

Rick Adams and Karl Friston @ UCL - Wellcome Trust Centre for Neuroimaging
Mina Aliakbari Khoei and Anna Montagnini - FACETS-ITN Marie Curie Training

LACONEU 2019: 5th Latin-American Summer School in Computational Neuroscience, 17/1/2019

LP (2015) "Sparse models" in Biologically Inspired Computer Vision

and at this instant, the sensed physical position is lagging behind (as represented here by $ au_s \cdot v 1~m$ ), that is, approximately at $45$ degrees of eccentricity (red dotted line),
while the position at the moment of emitting the motor command will be $.8~m$ ahead of its present physical position ($ au_m \cdot v$).
As a consequence, note that the player's gaze is directed to the ball at its present position (red line), in anticipatory fashion. Optimal control directs action (future motion of the eye) to the expected position (red dashed line) of the ball in the future --- and the racket (black dashed line) to the expected position of the ball when motor commands reach the periphery (muscles). This is obviously an interesting challenge for modelling an optimal control theory.

Outline

About Dynamics, vision and neurons

Active Inference

Back to the present

Perspectives ?

in a first study, we have proposed that PERCEPTION (following Helmhotz 1866) is an active process of hypothesis testing by which we seek to confirm our predictive models of the (hidden) world: Active inference: (cite TITLE). In theory, one could find any prior to fit any experimental data, but the beauty of the theory comes from the simplicity of the models chosen to model the data at hand, such as saccades...
... and even better if these models may find a possible correspondance into the neural anatomy and explain some deviation to a control behaviour, such as that we modelled for understanding some aspects of the EMs of schizophrenic patients
Today, I will again focus on the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. Specifically, we consider delays in the visuo-oculomotor loop and their implications for active inference and I will present the results presented in the following paper (show TITLE).

This schematic shows the dependencies among various quantities modelling exchanges of an agent with the environment. It shows the states of the environment and the system in terms of a probabilistic dependency graph, where connections denote directed (causal) dependencies. The quantities are described within the nodes of this graph -- with exemplar forms for their dependencies on other variables.
Hidden (external) and internal states of the agent are separated by action and sensory states. Both action and internal states -- encoding a conditional probability density function over hidden states -- minimise free energy. Note that hidden states in the real world and the form of their dynamics can be different from that assumed by the generative model; (this is why hidden states are in bold. )

This figure reports the conditional estimates of hidden states and causes during the simulation of pursuit initiation, using a single rightward (positive) sweep of a visual target, while compensating for sensory motor delays. We will use the format of this figure in subsequent figures: the upper left panel shows the predicted sensory input (coloured lines) and sensory prediction errors (dotted red lines) along with the true values (broken black lines). Here, we see horizontal excursions of oculomotor angle (upper lines) and the angular position of the target in an intrinsic frame of reference (lower lines). This is effectively the distance of the target from the centre of gaze and reports the spatial lag of the target that is being followed (solid red line). One can see clearly the initial displacement of the target that is suppressed after a few hundred milliseconds. The sensory predictions are based upon the conditional expectations of hidden oculomotor (blue line) and target (red line) angular displacements shown on the upper right. The grey regions correspond to 90% Bayesian confidence intervals and the broken lines show the true values of these hidden states. One can see the motion that elicits following responses and the oculomotor excursion that follows with a short delay of about 64ms. The hidden cause of these displacements is shown with its conditional expectation on the lower left. The true cause and action are shown on the lower right. The action (blue line) is responsible for oculomotor displacements and is driven by the proprioceptive prediction errors.

This figure illustrates the effects of sensorimotor delays on pursuit initiation (red lines) in relation to compensated (optimal) active inference -- as shown in the previous figure (blue lines). The left panels show the true (solid lines) and estimated sensory input (dotted lines), while action is shown in the right panels. Under pure sensory delays (top row), one can see clearly the delay in sensory predictions, in relation to the true inputs. The thicker (solid and dotted) red lines correspond respectively to (true and predicted) proprioceptive input, reflecting oculomotor displacement. The middle row shows the equivalent results with pure motor delays and the lower row presents the results with combined sensorimotor delays. Of note here is the failure of optimal control with oscillatory fluctuations in oculomotor trajectories, which become unstable under combined sensorimotor delays.

This figure illustrates the effects of sensorimotor delays on pursuit initiation (red lines) in relation to compensated (optimal) active inference -- as shown in the previous figure (blue lines). The left panels show the true (solid lines) and estimated sensory input (dotted lines), while action is shown in the right panels. Under pure sensory delays (top row), one can see clearly the delay in sensory predictions, in relation to the true inputs. The thicker (solid and dotted) red lines correspond respectively to (true and predicted) proprioceptive input, reflecting oculomotor displacement. The middle row shows the equivalent results with pure motor delays and the lower row presents the results with combined sensorimotor delays. Of note here is the failure of optimal control with oscillatory fluctuations in oculomotor trajectories, which become unstable under combined sensorimotor delays.

This figure illustrates the effects of sensorimotor delays on pursuit initiation (red lines) in relation to compensated (optimal) active inference -- as shown in the previous figure (blue lines). The left panels show the true (solid lines) and estimated sensory input (dotted lines), while action is shown in the right panels. Under pure sensory delays (top row), one can see clearly the delay in sensory predictions, in relation to the true inputs. The thicker (solid and dotted) red lines correspond respectively to (true and predicted) proprioceptive input, reflecting oculomotor displacement. The middle row shows the equivalent results with pure motor delays and the lower row presents the results with combined sensorimotor delays. Of note here is the failure of optimal control with oscillatory fluctuations in oculomotor trajectories, which become unstable under combined sensorimotor delays.

This figure illustrates the effects of sensorimotor delays on pursuit initiation (red lines) in relation to compensated (optimal) active inference -- as shown in the previous figure (blue lines). The left panels show the true (solid lines) and estimated sensory input (dotted lines), while action is shown in the right panels. Under pure sensory delays (top row), one can see clearly the delay in sensory predictions, in relation to the true inputs. The thicker (solid and dotted) red lines correspond respectively to (true and predicted) proprioceptive input, reflecting oculomotor displacement. The middle row shows the equivalent results with pure motor delays and the lower row presents the results with combined sensorimotor delays. Of note here is the failure of optimal control with oscillatory fluctuations in oculomotor trajectories, which become unstable under combined sensorimotor delays.

This figure uses the same format as the previous figure -- the only difference is that the target motion has been rectified so that it is (approximately) hemi-sinusoidal. The thing to note here is that the improved accuracy of the pursuit previously apparent at the onset of the second cycle of motion has now disappeared -- because active inference does not have access to the immediately preceding trajectory. This failure of an anticipatory improvement in tracking is contrary to empirical predictions. \item \odot
the generative model has been equipped with a second hierarchical level that contains hidden states, modelling latent periodic behaviour of the (hidden) causes of target motion. With this addition, the improvement in pursuit accuracy apparent at the onset of the second cycle of motion is reinstated. This is because the model has an internal representation of latent causes of target motion that can be called upon even when these causes are not expressed explicitly in the target trajectory.

This figure uses the same format as the previous figure -- the only difference is that the target motion has been rectified so that it is (approximately) hemi-sinusoidal. The thing to note here is that the improved accuracy of the pursuit previously apparent at the onset of the second cycle of motion has now disappeared -- because active inference does not have access to the immediately preceding trajectory. This failure of an anticipatory improvement in tracking is contrary to empirical predictions. \item \odot
the generative model has been equipped with a second hierarchical level that contains hidden states, modelling latent periodic behaviour of the (hidden) causes of target motion. With this addition, the improvement in pursuit accuracy apparent at the onset of the second cycle of motion is reinstated. This is because the model has an internal representation of latent causes of target motion that can be called upon even when these causes are not expressed explicitly in the target trajectory.

This figure uses the same format as the previous figure -- the only difference is that the target motion has been rectified so that it is (approximately) hemi-sinusoidal. The thing to note here is that the improved accuracy of the pursuit previously apparent at the onset of the second cycle of motion has now disappeared -- because active inference does not have access to the immediately preceding trajectory. This failure of an anticipatory improvement in tracking is contrary to empirical predictions. \item \odot
the generative model has been equipped with a second hierarchical level that contains hidden states, modelling latent periodic behaviour of the (hidden) causes of target motion. With this addition, the improvement in pursuit accuracy apparent at the onset of the second cycle of motion is reinstated. This is because the model has an internal representation of latent causes of target motion that can be called upon even when these causes are not expressed explicitly in the target trajectory.

This figure uses the same format as the previous figure -- the only difference is that the target motion has been rectified so that it is (approximately) hemi-sinusoidal. The thing to note here is that the improved accuracy of the pursuit previously apparent at the onset of the second cycle of motion has now disappeared -- because active inference does not have access to the immediately preceding trajectory. This failure of an anticipatory improvement in tracking is contrary to empirical predictions. \item \odot
the generative model has been equipped with a second hierarchical level that contains hidden states, modelling latent periodic behaviour of the (hidden) causes of target motion. With this addition, the improvement in pursuit accuracy apparent at the onset of the second cycle of motion is reinstated. This is because the model has an internal representation of latent causes of target motion that can be called upon even when these causes are not expressed explicitly in the target trajectory.

Outline

About Dynamics, vision and neurons

Active Inference

Back to the present

Perspectives ?

Khoei, Masson and LP (2017) PLoS CB

so let's go back on earth

... but first let now apply this model compensating for the aforementioned visual delays using a well described visual illusion: the flash-lag effect:

a first stimulus moves continuously across the screen along the central horizontal axis. In the FLE, as this moving stimulus reaches the center of the screen, a second stimulus is flashed just above it and in perfect vertical alignment. Despite the fact that the respective horizontal positions of each stimulus are physically identical when the flash occurs, the moving stimulus is most often perceived ahead of the flashed one.

debate for 80 years revived recently

motion extrapolation
differential latency
post-diction

we propose to extend the hypothesis previously proposed by Nihjawan that this effect is caused by the extrapolation of the stimulus' motion to compensate for the neural delay. However, this hypothesis was challenged by other hypothesis that this effect is due to either anatomy (differential latencies) or to the way visual awareness processes the sequence of events (the post-diction from Eagleman)

As a matter of fact, the motion extrapolation hypothesis was challenged because you can notice that the FLE is still present at initiation of the movement but this effect is not seen if the moving dot abruptly stops at the moment of the flash

Back to the present

Khoei, Masson and LP (2017) PLoS CB

Our model is very simple: Following what Nihjawan called a "diagonal model", we have proposed to extend a motion-based predictive coding model by including the known sensory delay in the prediction-estimation loop
More formally, the estimated state vector $z = {x, y, u, v}$ is composed of the 2D position ($x$ and $y$) and velocity ($u$ and $v$) of a (possibly moving) stimulus. As such, we extend Nijhawan's diagonal model into a classical Markov chain in order to take into account the known neural delay $ au$: At time $t$, information is integrated until time $t- au$, using a Markov chain and a model of state transitions $p(z_t |z_{t-\delta t})$ such that one can infer the state until the last accessible information $p(z_{t- au} | I_{0:(t- au)})$. This information can then be ``pushed'' forward in time by predicting its trajectory from $t- au$ to $t$. Interestingly, we use the same model of state transition at the time scale of the delay, that is, $p(z_t | z_{t- au})$.
Importantly, the model for transition is based on a simple model of the transport of visual information knowing the velocity. Indeed, knowing $z(t-\Delta t)$ we may infer the future position of this motion at time $t$ by using Newton's first law of mechanics. The precision of the state transition (as given by the shaded area) tunes the strength of this prediction as a diffusion parameter.
Such a model can be implemented in a two-layered neural network including a source (input) and a target (predictive) layer~\citep{KaplanKhoei14}. The source layer receives the delayed sensory information and encodes both position and velocity topographically on the different retinotopic maps of each layer. Notice that here, for simplicity, we show only one 2D map of the motions $(x, v)$. Crucially, the delay compensation in this motion-based prediction model, is simply implemented by connecting each source neuron to a predictive neuron corresponding to the corrected position of stimulus $(x+v\cdot au, v)$ in the target layer. The precision of this anisotropic connectivity map can be tuned by the width of convergence from the source to the target populations. We will see on Friday that this property (that it is implemented in the connectivity) extends to more complex, multi-layered systems which may generalize to more complex trajectories.

Back to the present

Khoei, Masson and LP (2017) PLoS CB

Our model is very simple: Following what Nihjawan called a "diagonal model", we have proposed to extend a motion-based predictive coding model by including the known sensory delay in the prediction-estimation loop
More formally, the estimated state vector $z = {x, y, u, v}$ is composed of the 2D position ($x$ and $y$) and velocity ($u$ and $v$) of a (possibly moving) stimulus. As such, we extend Nijhawan's diagonal model into a classical Markov chain in order to take into account the known neural delay $ au$: At time $t$, information is integrated until time $t- au$, using a Markov chain and a model of state transitions $p(z_t |z_{t-\delta t})$ such that one can infer the state until the last accessible information $p(z_{t- au} | I_{0:(t- au)})$. This information can then be ``pushed'' forward in time by predicting its trajectory from $t- au$ to $t$. Interestingly, we use the same model of state transition at the time scale of the delay, that is, $p(z_t | z_{t- au})$.
Importantly, the model for transition is based on a simple model of the transport of visual information knowing the velocity. Indeed, knowing $z(t-\Delta t)$ we may infer the future position of this motion at time $t$ by using Newton's first law of mechanics. The precision of the state transition (as given by the shaded area) tunes the strength of this prediction as a diffusion parameter.
Such a model can be implemented in a two-layered neural network including a source (input) and a target (predictive) layer~\citep{KaplanKhoei14}. The source layer receives the delayed sensory information and encodes both position and velocity topographically on the different retinotopic maps of each layer. Notice that here, for simplicity, we show only one 2D map of the motions $(x, v)$. Crucially, the delay compensation in this motion-based prediction model, is simply implemented by connecting each source neuron to a predictive neuron corresponding to the corrected position of stimulus $(x+v\cdot au, v)$ in the target layer. The precision of this anisotropic connectivity map can be tuned by the width of convergence from the source to the target populations. We will see on Friday that this property (that it is implemented in the connectivity) extends to more complex, multi-layered systems which may generalize to more complex trajectories.

Let me show you now the results of our simulations on the standard flash-lag stimulus by first showing the results of a predictive system with no delay compensation. We here simply show the source layer in the prediction model which tries to track moving patterns but do not compensate for the known delay.
We observe that the moving dot is correctly tracked and the flash correctly detected. I have slowed down the speed of the movie to show that relative to each other, the flash and moving dot appear at the same position at the time of the flash.
The situation is different in the motion-based predictive system : in this slowed down movie, at the time the flash appears, the dot's position was predicted ahead of the position of the flash, similarly to what is reported in psychophysics.
Moreover, we have predicted a similar effect with a large range of parameters of the stimulus, such as speed or contrast. But for the moment, let us focus on the reasons why the model shows a similar behavior

Let me show you now the results of our simulations on the standard flash-lag stimulus by first showing the results of a predictive system with no delay compensation. We here simply show the source layer in the prediction model which tries to track moving patterns but do not compensate for the known delay.
We observe that the moving dot is correctly tracked and the flash correctly detected. I have slowed down the speed of the movie to show that relative to each other, the flash and moving dot appear at the same position at the time of the flash.
The situation is different in the motion-based predictive system : in this slowed down movie, at the time the flash appears, the dot's position was predicted ahead of the position of the flash, similarly to what is reported in psychophysics.
Moreover, we have predicted a similar effect with a large range of parameters of the stimulus, such as speed or contrast. But for the moment, let us focus on the reasons why the model shows a similar behavior

Back to the present

Khoei, Masson and LP (2017) PLoS CB

For that, we replot the movies I have just shown by showing for the dot the Histogram of the estimated positions as a function of time for the source layer (Left) and the target layer (right). The left-hand column illustrates the predictive model before delay compensation. The right-hand column illustrates the motion extrapolation model with delay compensation. Histograms of the inferred horizontal positions (blueish bottom panel) and horizontal speed (redish top panel) are shown in columns as a function of time. A darker level corresponds to a higher probability, while a light color corresponds to an unlikely estimation. In particular, we focus on three particular epochs along the trajectory, corresponding to the standard, flash initiated and terminated cycles. The timing of these epochs flashes are indicated by dashed vertical lines. In dark, the physical time and in green the delayed input knowing $ au=100~ms$.
Activity in both models shows three different phases. First, there is a rapid build-up of the precision of the target after the first appearance of the moving dot (at $t=300~ms$). Consistently with the Frölich effect, the beginning of the trajectory is seen ahead of its physical position. During the second phase, the moving dot is correctly tracked as both its velocity and position are correctly inferred. In the source layer, there is no extrapolation and the trajectory follows the delayed trajectory of the dot (green dotted line). In the target layer, motion extrapolation correctly predicts the position at the present time and the position follows the actual physical position of the dot (black dotted line). Finally, the third phase corresponds to motion termination. The moving dot disappears and the corresponding activity vanishes in the source layer at $t=900~ms$. However, between $t=800~ms$ and $t=900~ms$, the dot position was extrapolated and predicted ahead of the terminal position. At $t=900~ms$, while motion information is absent, the position information is still transiently consistent and extrapolated using a broad, centered prior distribution of speeds. Although it is less precise, this position of the dot at flash termination is therefore not perceived as leading the flash.
Interestingly, and thanks to the reviewers of the paper, we could extend our results to the estimation of the dot position from the dMBP model during the motion reversal experiment. In the motion reversal experiment, the moving dot reverses its direction at the middle of the trajectory (i.e., at $t=500~ms$, as indicated by the mid-point vertical dashed line). In the left column (target layer) and as in previous slide, we show the histogram of inferred positions during the dot motion and a trace of its position with the highest probability as a function of time. As expected, results are identical to that in the previous slide in the first half period. At the moment of the motion reversal, the model output is consistent with previous psychophysical reports. First, the estimated position follows the extrapolated trajectory until the (delayed) sensory information about the motion reversal reaches the system (at $t=600~ms$, green vertical dashed line). Then, the velocity is quickly reseted and converges to the new (reversed) motion such that the estimated position ``jumps'' to a position corresponding to the updated velocity. In the right column (smoothed layer), we show the results of the same data after a smoothing operation of $ au_s=100~ms$ in subjective time. This different read-out from the inferred positions corresponds to the behavioral results obtained in some experiments, such as that from~\citet{Whitney98}.

Back to the present

Khoei, Masson and LP (2017) PLoS CB

For that, we replot the movies I have just shown by showing for the dot the Histogram of the estimated positions as a function of time for the source layer (Left) and the target layer (right). The left-hand column illustrates the predictive model before delay compensation. The right-hand column illustrates the motion extrapolation model with delay compensation. Histograms of the inferred horizontal positions (blueish bottom panel) and horizontal speed (redish top panel) are shown in columns as a function of time. A darker level corresponds to a higher probability, while a light color corresponds to an unlikely estimation. In particular, we focus on three particular epochs along the trajectory, corresponding to the standard, flash initiated and terminated cycles. The timing of these epochs flashes are indicated by dashed vertical lines. In dark, the physical time and in green the delayed input knowing $ au=100~ms$.
Activity in both models shows three different phases. First, there is a rapid build-up of the precision of the target after the first appearance of the moving dot (at $t=300~ms$). Consistently with the Frölich effect, the beginning of the trajectory is seen ahead of its physical position. During the second phase, the moving dot is correctly tracked as both its velocity and position are correctly inferred. In the source layer, there is no extrapolation and the trajectory follows the delayed trajectory of the dot (green dotted line). In the target layer, motion extrapolation correctly predicts the position at the present time and the position follows the actual physical position of the dot (black dotted line). Finally, the third phase corresponds to motion termination. The moving dot disappears and the corresponding activity vanishes in the source layer at $t=900~ms$. However, between $t=800~ms$ and $t=900~ms$, the dot position was extrapolated and predicted ahead of the terminal position. At $t=900~ms$, while motion information is absent, the position information is still transiently consistent and extrapolated using a broad, centered prior distribution of speeds. Although it is less precise, this position of the dot at flash termination is therefore not perceived as leading the flash.
Interestingly, and thanks to the reviewers of the paper, we could extend our results to the estimation of the dot position from the dMBP model during the motion reversal experiment. In the motion reversal experiment, the moving dot reverses its direction at the middle of the trajectory (i.e., at $t=500~ms$, as indicated by the mid-point vertical dashed line). In the left column (target layer) and as in previous slide, we show the histogram of inferred positions during the dot motion and a trace of its position with the highest probability as a function of time. As expected, results are identical to that in the previous slide in the first half period. At the moment of the motion reversal, the model output is consistent with previous psychophysical reports. First, the estimated position follows the extrapolated trajectory until the (delayed) sensory information about the motion reversal reaches the system (at $t=600~ms$, green vertical dashed line). Then, the velocity is quickly reseted and converges to the new (reversed) motion such that the estimated position ``jumps'' to a position corresponding to the updated velocity. In the right column (smoothed layer), we show the results of the same data after a smoothing operation of $ au_s=100~ms$ in subjective time. This different read-out from the inferred positions corresponds to the behavioral results obtained in some experiments, such as that from~\citet{Whitney98}.

Back to the present

Khoei, Masson and LP (2017) PLoS CB

For that, we replot the movies I have just shown by showing for the dot the Histogram of the estimated positions as a function of time for the source layer (Left) and the target layer (right). The left-hand column illustrates the predictive model before delay compensation. The right-hand column illustrates the motion extrapolation model with delay compensation. Histograms of the inferred horizontal positions (blueish bottom panel) and horizontal speed (redish top panel) are shown in columns as a function of time. A darker level corresponds to a higher probability, while a light color corresponds to an unlikely estimation. In particular, we focus on three particular epochs along the trajectory, corresponding to the standard, flash initiated and terminated cycles. The timing of these epochs flashes are indicated by dashed vertical lines. In dark, the physical time and in green the delayed input knowing $ au=100~ms$.
Activity in both models shows three different phases. First, there is a rapid build-up of the precision of the target after the first appearance of the moving dot (at $t=300~ms$). Consistently with the Frölich effect, the beginning of the trajectory is seen ahead of its physical position. During the second phase, the moving dot is correctly tracked as both its velocity and position are correctly inferred. In the source layer, there is no extrapolation and the trajectory follows the delayed trajectory of the dot (green dotted line). In the target layer, motion extrapolation correctly predicts the position at the present time and the position follows the actual physical position of the dot (black dotted line). Finally, the third phase corresponds to motion termination. The moving dot disappears and the corresponding activity vanishes in the source layer at $t=900~ms$. However, between $t=800~ms$ and $t=900~ms$, the dot position was extrapolated and predicted ahead of the terminal position. At $t=900~ms$, while motion information is absent, the position information is still transiently consistent and extrapolated using a broad, centered prior distribution of speeds. Although it is less precise, this position of the dot at flash termination is therefore not perceived as leading the flash.
Interestingly, and thanks to the reviewers of the paper, we could extend our results to the estimation of the dot position from the dMBP model during the motion reversal experiment. In the motion reversal experiment, the moving dot reverses its direction at the middle of the trajectory (i.e., at $t=500~ms$, as indicated by the mid-point vertical dashed line). In the left column (target layer) and as in previous slide, we show the histogram of inferred positions during the dot motion and a trace of its position with the highest probability as a function of time. As expected, results are identical to that in the previous slide in the first half period. At the moment of the motion reversal, the model output is consistent with previous psychophysical reports. First, the estimated position follows the extrapolated trajectory until the (delayed) sensory information about the motion reversal reaches the system (at $t=600~ms$, green vertical dashed line). Then, the velocity is quickly reseted and converges to the new (reversed) motion such that the estimated position ``jumps'' to a position corresponding to the updated velocity. In the right column (smoothed layer), we show the results of the same data after a smoothing operation of $ au_s=100~ms$ in subjective time. This different read-out from the inferred positions corresponds to the behavioral results obtained in some experiments, such as that from~\citet{Whitney98}.

Outline

About Dynamics, vision and neurons

Active Inference

Back to the present

Perspectives ?

Chemla, Reynaud, di Volo, Zerlaut, LP, Destexhe, Chavane (2019) in revision

Figure 1: Experimental protocol and time-sequence of the cortical response to the long-range apparent motion (lrAM). A: Two-step lrAM stimuli are presented to two awake fixating monkeys in their bottom left visual field, while recording in their right visual cortex using VSDI. B: Spatio-temporal characteristics of lrAM stimuli, i.e. duration (DUR) , interstimulus interval (ISI) and spatial interval (SI), were varied to cover a [5-66.6]°/s range of speed. C-E: Cortical representation of evoked VSDI activity as a function of time, in response to respectively, a 100 ms local stimulus in the down position, another one Explaining away ambiguous representations in V1 in the up position, and the sequence of these two stimuli (ISI = 50 ms and SI = 1°). The cortical area imaged is shown at upper left. The edge of the image color codes the retinotopic borders as represented in A such as the vertical meridian (magenta), eccentricities (green and blue). Scale bar: 2 mm; A: anterior, P: posterior, M: medial, L: lateral. Time in milliseconds after stimulus onset is shown at the top, while stimulation time is drawn at the bottom of each row (black lines). F: Activity pattern predicted by the linear combination in space and time of the response to stimulus 1 (row C) and the response to stimulus 2 (row D). G: Suppression pattern obtained by subtracting the observed apparent motion response (row E) and the linear prediction (row F). Red contours delimit amplitude activity above a certain threshold: 1 in panels C-F and -0.5 in panel G.

Figure 3: The apparent motion stimulus induces a systematic suppression wave. Spatio-temporal representation of VSDI responses to two-stroke apparent motion stimuli for three different speed (6.6°/s, 10°/s and 33.3 °/s) and two animals ( A: monkey WA, B: monkey BR). The upper rows of A and B represent the observed response and the lower rows the non-linearities of the response (observed - linear prediction). Estimates of speed propagation are reported on each ST diagram (black stairs-step are contours at threshold level, slanting lines are the slope of the linear regression). Similar values are observed for both the observed activity and the non-linearities.

Chemla, Reynaud, di Volo, Zerlaut, LP, Destexhe, Chavane (2019) in revision

Benvenuti, Taouali, LP, Chavane () in prep

Muller, Chavane, Reynolds, Sejnowski (2018) Cortical travelling waves: mechanisms and computational principles, Nature Reviews Neuroscience 19 (5), 255